A driving path selection method and device, electronic equipment and storage medium

Abstract

The application discloses a driving path selection method and device, electronic equipment and a storage medium, and relates to the technical field of intelligent driving. In the application, current position information of a vehicle and target position information of a driving destination are acquired; then, at least one candidate driving path is generated based on the current position information and the target position information; further, expected driving costs required by the vehicle in the at least one candidate driving path are respectively determined based on path feature sets of the at least one candidate driving path; finally, a target driving path meeting a preset driving cost condition is screened out from the at least one candidate driving path based on the obtained at least one expected driving cost. In this way, the target driving path with the least path driving cost can be accurately screened out.

Description

Technical Field

[0001] This application relates to the field of intelligent driving technology, and in particular to a driving path selection method, device, electronic device and storage medium. Background Technology

[0002] In recent years, with the increasing demand for energy conservation and emission reduction, new energy vehicles have developed rapidly. Compared with traditional fuel vehicles, they are more conducive to saving energy and reducing the emission of harmful gases. Therefore, under the pressure of energy and environmental protection, new energy vehicles will undoubtedly become an important direction for the future development of the vehicle industry.

[0003] Currently, in the research process of new energy vehicles, how to improve the energy utilization rate of vehicles as much as possible while ensuring vehicle power performance and solving the problem of vehicle energy replenishment has become an increasingly important issue that needs to be addressed.

[0004] Furthermore, in order to effectively solve the above problems, by formulating energy management strategies, it is possible to effectively achieve reasonable power distribution among vehicle power components and reduce vehicle fuel consumption, thereby improving the energy utilization rate of the vehicle and solving the problem of vehicle energy replenishment.

[0005] In the existing technology, energy management strategies can be mainly divided into four types: energy management strategies based on deterministic rules, energy management strategies based on fuzzy rules, energy management strategies based on instantaneous optimization, and energy management strategies based on global optimization. However, the above four energy management strategies mainly focus on the energy distribution between vehicle power components, and on maximizing the fuel efficiency of the engine while meeting the vehicle's required torque, without considering the impact of future changes in vehicle driving conditions on the formulation of power energy management strategies.

[0006] In light of this, to further enhance the energy-saving effect of energy management strategies, improved energy management strategies that integrate future vehicle driving conditions have emerged. These improved energy management strategies primarily employ two methods for obtaining future driving conditions: acquiring future driving conditions based on intelligent transportation systems and predicting future driving conditions based on historical driving conditions.

[0007] Next, after obtaining the future driving conditions, algorithms such as dynamic programming, stochastic dynamic programming, and model predictive control can be used to optimize the energy distribution of the vehicle's current and future power components, thereby determining the energy management strategy for new energy vehicles.

[0008] However, the improved energy management strategy described above is still unable to accurately obtain the path cost of each candidate vehicle's path, thus failing to select the target vehicle's path with the lowest path cost from among the candidate vehicle's paths.

[0009] Therefore, the above method cannot accurately select the target vehicle's travel route with the lowest travel cost. Summary of the Invention

[0010] This application provides a method, apparatus, electronic device, and storage medium for selecting a driving route, which can accurately filter out the target driving route with the lowest driving cost.

[0011] In a first aspect, embodiments of this application provide a method for selecting a driving route, the method comprising:

[0012] Step 1: Obtain the vehicle's current location information and the target location information of the driving destination;

[0013] Step 2: Based on the current location information and the target location information, generate at least one candidate driving path;

[0014] Step 3: Based on the path feature sets of at least one candidate driving path, determine the expected driving cost required by the vehicle on each of the at least one candidate driving path.

[0015] Step 4: Based on the obtained at least one expected driving cost, select the target driving path that meets the preset driving cost conditions from at least one candidate driving path.

[0016] Secondly, embodiments of this application also provide a driving route selection device, the device comprising:

[0017] The response module is used to obtain the vehicle's current location information and the target location information of the driving destination;

[0018] The generation module is used to generate at least one candidate driving path based on the current location information and the target location information;

[0019] The determination module is used to determine the expected driving cost required by the vehicle on each of the at least one candidate driving path, based on the path feature set of each of the at least one candidate driving path.

[0020] The filtering module is used to filter out target driving routes that meet preset driving cost conditions from at least one candidate driving route based on at least one obtained expected driving cost.

[0021] In one possible embodiment, during step three, the determining module is specifically used for:

[0022] For at least one candidate driving route, perform the following operations respectively:

[0023] From the path feature set of a candidate driving path, obtain a subset of the slope information of the candidate driving path;

[0024] Based on the slope of each road included in the obtained slope information subset and the slope interval to which each road belongs, a candidate driving route is divided to obtain at least one candidate driving segment.

[0025] Determine the required sub-energy consumption for each of at least one candidate travel segment, and based on the obtained at least one sub-energy consumption, determine the energy consumption cost of a candidate travel route;

[0026] Based on energy consumption costs and road travel costs corresponding to a candidate travel path, the expected travel cost required for a vehicle on a candidate travel path is determined.

[0027] In one possible embodiment, the slope information subset includes the slope information collected by each road information sampling point on a candidate driving path;

[0028] After obtaining a subset of slope information for a candidate driving path, the determining module is further configured to:

[0029] For each slope setting, perform the following operations:

[0030] Obtain the original road segment containing slope information;

[0031] If there are depressions in the original road segment that meet the preset depression conditions, the original road segment is smoothed to obtain the corresponding target road segment.

[0032] In one possible embodiment, when dividing a candidate driving path based on the slope of each road included in the obtained slope information subset and the slope interval to which each road belongs, and obtaining at least one candidate driving segment, the determining module is specifically used for:

[0033] For each subset of slope information, for every at least two adjacent pairs of slope information, perform the following operations:

[0034] If at least two adjacent slope information entries contain the same road slope and belong to the same slope range, then the target road segments corresponding to the at least two slope information entries will be merged to obtain the corresponding candidate driving road segments.

[0035] In one possible embodiment, after merging the target road segments contained in at least two slope information sets to obtain corresponding candidate driving road segments, the determining module is further configured to:

[0036] Based on the road slopes of at least two target road segments, the road slope of the candidate driving road segment is obtained, and based on the road lengths of at least two target road segments, the road length of the candidate driving road segment is obtained.

[0037] In one possible embodiment, when determining the required sub-energy consumption for each of the at least one candidate travel segment, the determining module is specifically configured to:

[0038] For at least one candidate travel segment, perform the following operations respectively:

[0039] Based on the path feature set of a candidate driving route, the driving influencing factors of a candidate driving segment are obtained; where the driving influencing factors represent: the environmental information of a candidate driving segment;

[0040] Based on driving-related factors, the vehicle speed and length of a candidate driving segment are set for each candidate driving segment to determine the sub-energy consumption required by the vehicle in that segment.

[0041] In one possible embodiment, during step four, the filtering module is specifically used for:

[0042] Based on at least one expected driving cost, obtain the driving cost ranking order corresponding to at least one candidate driving path;

[0043] Based on at least one obtained ranking of driving costs, a target driving path that meets the preset driving cost conditions is selected from at least one candidate driving path.

[0044] Thirdly, an electronic device is proposed, comprising a processor and a memory, wherein the memory stores program code that, when executed by the processor, causes the processor to perform the steps of the driving path selection method described in the first aspect.

[0045] Fourthly, a computer-readable storage medium is proposed, comprising program code that, when executed on an electronic device, causes the electronic device to perform the steps of the driving path selection method described in the first aspect.

[0046] Fifthly, a computer program product is provided, which, when invoked by a computer, causes the computer to execute the driving path selection method steps as described in the first aspect.

[0047] The beneficial effects of this application are as follows:

[0048] In the driving route selection method provided in this application embodiment, the current location information of the vehicle and the target location information of the driving destination are obtained; then, based on the current location information and the target location information, at least one candidate driving route is generated; further, based on the path feature set of each of the at least one candidate driving route, the expected driving cost required by the vehicle on each of the at least one candidate driving route is determined; finally, based on the obtained at least one expected driving cost, a target driving route that meets the preset driving cost condition is selected from the at least one candidate driving route.

[0049] This approach determines the expected travel cost of a vehicle on each of the at least one candidate travel path based on the path feature set of each candidate travel path. Then, based on the obtained expected travel cost, a target travel path that meets the preset travel cost conditions is selected from the at least one candidate travel path. This avoids the technical drawback of existing technologies that cannot accurately obtain the path travel cost of each candidate vehicle travel path. Therefore, it is used to accurately select the target travel path with the lowest path travel cost.

[0050] Furthermore, other features and advantages of this application will be set forth in the following description and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0052] Figure 1 An exemplary schematic diagram of the architecture of a path selection system applicable to embodiments of this application is shown;

[0053] Figure 2 An exemplary illustration shows a flowchart of a driving route selection method provided in an embodiment of this application;

[0054] Figure 3 An exemplary illustration shows a specific application scenario diagram of generating candidate driving paths provided by an embodiment of this application;

[0055] Figure 4 An exemplary illustration shows a flowchart of a method for determining the expected driving cost of a candidate driving path according to an embodiment of this application;

[0056] Figure 5 An exemplary schematic diagram of a road filtering method provided in an embodiment of this application is shown;

[0057] Figure 6 An exemplary illustration shows a logical diagram of a reconstructed road provided in an embodiment of this application;

[0058] Figure 7 An exemplary schematic diagram of a logic diagram for filtering a target driving path is shown in an embodiment of this application;

[0059] Figure 8 An exemplary embodiment of this application provides a method based on... Figure 2 Schematic diagram of specific application scenarios;

[0060] Figure 9 An exemplary schematic diagram of a driving route selection device provided in an embodiment of this application is shown;

[0061] Figure 10 An exemplary schematic diagram of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this application. Obviously, the described embodiments are only some embodiments of the technical solutions of this application, and not all embodiments. Based on the embodiments recorded in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the technical solutions of this application.

[0063] It should be noted that in the description of this application, "multiple" is understood as "at least two". "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A connected to B can represent: A and B directly connected, or A and B connected through C. Furthermore, in the description of this application, terms such as "first" and "second" are used only for descriptive purposes and should not be construed as indicating or implying relative importance or order.

[0064] First, the design concept of the embodiments of this application will be briefly introduced below:

[0065] With the advancement of carbon emission reduction and the rapid development of new energy technologies, the sales of new energy vehicles have increased significantly. Therefore, unlike the problems of traditional fuel vehicles, the energy supply and energy efficiency of new energy vehicles are more likely to attract attention from all parties.

[0066] Among them, predictive energy management is an innovative solution to address the optimal energy management problem of long-haul heavy-duty trucks, especially new energy range-extended battery-swapping heavy-duty trucks. It is used to meet users' core needs for battery swapping and charging, solve pain points such as range anxiety, and provide drivers with the best overall cost solution.

[0067] It is not difficult to see that how to reconstruct the road ahead of the vehicle based on the road planning information sent by the vehicle navigation system, and how to simplify and merge the route information for calculating the vehicle's energy consumption and cost on that route; in addition, how to select the most cost-effective route based on the planning information of several routes from the starting point to the destination sent by the vehicle navigation system has become an urgent problem to be solved.

[0068] In view of this, in order to accurately select the target driving route with the lowest driving cost, this application proposes a driving route selection method, which specifically includes: obtaining the current location information of the vehicle and the target location information of the driving destination; then, generating at least one candidate driving route based on the current location information and the target location information; further, determining the expected driving cost required by the vehicle on each of the at least one candidate driving route based on the path feature set of each of the at least one candidate driving route; finally, selecting the target driving route that meets the preset driving cost condition from the at least one candidate driving route based on the obtained at least one expected driving cost.

[0069] In particular, the preferred embodiments of this application will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments of this application and the features in the embodiments can be combined with each other without conflict.

[0070] See Figure 1 The diagram shown is a schematic of an optional route selection system architecture provided in an embodiment of this application. The system architecture includes: a navigation application (APP) 101, a predictive energy management controller 102, a vehicle controller 103, and a map box 104. The navigation APP 101, the vehicle controller 103, and the map box 104 can all exchange data or information with the predictive energy management controller 102.

[0071] For example, after the driver inputs their destination through the navigation app 101, the app can generate several planned driving routes, and then send the route information of each route to the predictive energy management controller 102. Next, the predictive energy management controller 102 calculates the energy consumption and usage cost of each route based on the received route information. Further, after the calculation, the predictive energy management controller 102 sends the energy consumption and usage cost corresponding to each route back to the navigation app 101, so that the driver can choose a route after comprehensively considering factors such as driving time and cost. The navigation app 101 then sends the route information selected by the driver to the predictive energy management controller 102. Further, on the navigation route selected by the driver, the predictive energy management controller 102 uses the map box 104 (employing an Advanced Driver Assistance System) to... The ADASIS protocol (Motorola CAN message signal bit rules) is used to send road information (length, slope, etc.) within a certain number of kilometers ahead of the vehicle. It calculates the planned speed and gear when the vehicle reaches the next destination and sends them to the navigation APP 101 and the vehicle controller 103. Finally, the navigation APP 101 can display the planned speed and gear on the vehicle screen, and the vehicle controller 103 converts the planned speed into drive torque to make the vehicle reach the planned speed and at the same time performs a gear shift operation to put the vehicle in the planned gear.

[0072] The predictive energy management method provided by the exemplary embodiments of this application will be described below in conjunction with the above system architecture and with reference to the accompanying drawings. It should be noted that the above system architecture is only shown for the purpose of understanding the spirit and principles of this application, and the embodiments of this application are not limited in any way.

[0073] See Figure 2 The diagram shown is a flowchart of a driving route selection method provided in this application embodiment. Taking a route selection system as an example, the specific implementation process of this method is as follows:

[0074] S201: Obtain the vehicle's current location information and the target location information of the driving destination.

[0075] For example, when performing step S201, after the vehicle's automatic navigation is turned on, the route selection system can obtain the target location of the driving destination by inputting the driving destination on the vehicle's screen, and obtain the vehicle's current location by using Over-the-Air (OTA) technology to download navigation maps.

[0076] It should be noted that the above is only an example. In this embodiment of the application, the execution of the above method is not limited. That is, it can be the vehicle's own navigation or a mobile application, etc.

[0077] S202: Generate at least one candidate driving path based on the current location information and the target location information.

[0078] For details, please refer to Figure 3 As shown, when executing step S202, after obtaining the current location information of the vehicle and the target location information of the driving destination, the route selection system can parse the current location information and the target location information to obtain the corresponding current location of the vehicle and the destination location, so that the navigation APP in the route selection system can generate at least one candidate driving route based on the current location of the vehicle and the destination location.

[0079] S203: Based on the path feature sets of at least one candidate driving path, determine the expected driving cost required by the vehicle on each of the at least one candidate driving path.

[0080] Specifically, in step S203, after generating at least one candidate driving route through the navigation APP, the route selection system can determine the expected driving cost required by the vehicle on each of the at least one candidate driving route based on the path feature sets (i.e., path information) of each of the at least one candidate driving route, including information such as slope and vehicle speed contained in each of the obtained path feature sets. For the at least one candidate driving route, refer to... Figure 4 As shown, perform the following operations respectively:

[0081] S401: Obtain a subset of slope information for a candidate driving path from the path feature set of a candidate driving path.

[0082] The slope information subset includes the slope information collected by each road information sampling point on the candidate driving path, and each slope information includes at least: the road slope and the road segment length collected by each road information sampling point; in addition, each road information sampling point can collect the road condition information of the above-mentioned candidate driving path completely.

[0083] Optional, see below Figure 5As shown, after the path selection system obtains the slope information collected by each road information sampling point on a candidate driving path from the path feature set of a candidate driving path (i.e., obtains the slope information subset of the above-mentioned candidate driving path), it can perform the following operations for each slope information: obtain the original road segment contained in a slope information; if there is a concave road in the original road segment that meets the preset concavity conditions, then the original road segment is smoothed to obtain the corresponding target road segment. For example, if the original road segment contained in the above-mentioned slope information is a gentle road, and there is a small concave road on the gentle road, after filtering, the entire road becomes a gentle road.

[0084] It is easy to see that, based on the road filtering rules mentioned above, the uneven road in front of the vehicle is filtered out without affecting the overall slope change trend of the road ahead, which facilitates the control and management of road reconstruction.

[0085] S402: Based on the slope of each road included in the obtained slope information subset and the slope interval to which each road belongs, a candidate driving route is divided to obtain at least one candidate driving segment.

[0086] Specifically, when executing step S402, after obtaining the road slopes contained in each slope information in the slope information subset, the path selection system can determine the slope interval to which each road slope belongs based on the preset slope interval division rules, thereby dividing a candidate driving path (i.e. road reconstruction) and obtaining at least one candidate driving segment.

[0087] For example, when the road slope is recorded as positive and greater than the upper limit slope angle, it indicates that the road corresponding to the corresponding road information sampling point is uphill; when the road slope is recorded as negative and less than the lower limit slope angle, it indicates that the road corresponding to the corresponding road information sampling point is downhill. The preset slope interval division rule is as follows: if the road slope is less than the lower limit slope threshold, the road classification value is 0, that is, it belongs to the first slope interval; if the road slope is between the lower limit slope threshold and the upper limit slope threshold, the road classification value is 1, that is, it belongs to the second slope interval; if the road slope is greater than the upper limit slope threshold, the road classification value is 2, that is, it belongs to the third slope interval.

[0088] Therefore, based on the preset slope interval division rules, the roads corresponding to the road information sampling points can be accurately classified. (Optional, see [reference]). Figure 6 As shown, in order to reduce the number of roads, for each at least two adjacent slope information, the following operations are performed: if the road slopes contained in the at least two adjacent slope information are in the same slope range, then the target road segments corresponding to the at least two slope information are merged to obtain the corresponding candidate driving road segments.

[0089] For example, assuming the path selection system determines that the road slopes contained in the three adjacent slope information are all classified as belonging to the first slope interval, the road segments corresponding to the three adjacent slope information can be merged to obtain the corresponding candidate driving road segments.

[0090] Furthermore, after merging the target road segments corresponding to at least two slope information points to obtain corresponding candidate driving road segments (i.e., after completing road reconstruction), the path selection system can use the predictive energy management controller to obtain the road segment slope of the candidate driving road segment based on the road slopes of the at least two target road segments, and the road segment length of the candidate driving road segment based on the road lengths of the at least two target road segments. It should be noted that the calculation formulas for the road segment slope and the road segment length of the candidate driving road segment are as follows:

[0091]

[0092]

[0093] Where, θ Ave The slope of the candidate driving segment is represented by θ, where n represents the number of adjacent segments corresponding to slope information belonging to the same slope range. i L represents the road slope of the i-th road segment among n slope information segments; Ave L represents the length of the candidate driving segment. i This represents the road length of the segment corresponding to the i-th slope among the n slope information segments.

[0094] S403: Determine the sub-energy consumption required for each of the at least one candidate driving segment, and determine the energy consumption cost of the candidate driving route based on the obtained at least one sub-energy consumption.

[0095] In one possible implementation, when executing step S403, after obtaining at least one candidate driving segment, the route selection system can perform the following operations for each candidate driving segment: based on the path feature set of the candidate driving path, obtain the driving influencing factors of a candidate driving segment; wherein, the driving influencing factors represent: environmental information of a candidate driving segment; further, based on the driving influencing factors, the vehicle driving speed set corresponding to the slope type of the candidate driving segment and the segment length of the candidate driving segment, determine the sub-energy consumption required by the vehicle in the candidate driving segment.

[0096] For example, the energy consumption cost of a vehicle can be obtained by simplifying the road model of the candidate driving segment, considering only the longitudinal dynamics of the vehicle. The vehicle speed is set to the speed limit value of the road segment, and the congestion-recommended speed is adopted when there is congestion. Different values ​​of tire rolling resistance coefficient are assigned according to driving influencing factors (such as weather factors).

[0097] F=δma+mgsinα+μmgcosα+C d AV 2 / 21.15

[0098] Where F represents the vehicle's driving force, and the terms added on the right represent: vehicle acceleration resistance, vehicle slope resistance, vehicle rolling resistance, and vehicle wind resistance, respectively; δ represents the rotational mass inertia coefficient; m represents the vehicle's mass; a represents the vehicle's acceleration; g represents gravitational acceleration; α represents the road slope of the candidate driving segment; μ represents the rolling resistance coefficient; A represents the vehicle's frontal area; and C... d V represents the drag coefficient, and V represents the vehicle's speed.

[0099] Therefore, after the path selection system calculates the driving force F during vehicle movement based on the above formula, it can obtain the vehicle's driving power according to the following formula:

[0100] P = F × V

[0101] Where P represents the vehicle's driving power, F represents the vehicle's driving force, and V represents the vehicle's speed.

[0102] Next, after determining the vehicle's driving power, the route selection system can calculate the vehicle's motor power P′ according to a certain conversion factor. Then, based on the length of the candidate driving segment and the vehicle's driving speed, it can calculate the corresponding vehicle driving time and thus the vehicle's power consumption (i.e., sub-energy consumption) on the candidate driving segment.

[0103] Furthermore, the route selection system calculates the sub-energy consumption of each candidate driving segment on the candidate driving route according to the above method, and then superimposes the obtained sub-energy consumption to obtain the total energy consumption of the corresponding candidate driving route. Furthermore, after obtaining the total energy consumption of the candidate driving route, the system can allocate the total energy consumption to fuel replenishment and charging / swapping according to a certain proportion based on the information of energy replenishment points such as gas stations, battery swapping stations, and charging stations on the candidate driving route, and finally calculate the total energy consumption cost (i.e., energy consumption cost) of the route.

[0104] S404: Based on energy consumption costs and the road travel costs corresponding to a candidate travel path, determine the expected travel cost required for a vehicle on a candidate travel path.

[0105] Specifically, during step S404, after obtaining the energy consumption cost of a candidate driving route, the route selection system can also determine the road driving cost required for the vehicle to travel on the candidate driving route based on the road segment length. Therefore, based on the obtained energy consumption cost and road driving cost, the expected driving cost required for the vehicle on the candidate driving route is determined, which is the sum of the energy consumption cost and the road driving cost. It should be noted that the formula for calculating the expected driving cost is as follows:

[0106] W Y =W N +W L

[0107] Among them, W Y W represents the expected operating cost of a vehicle. N W represents the energy consumption cost of a vehicle. L This indicates the cost of driving a vehicle on the road.

[0108] Therefore, based on the above-described method steps, the route selection system can obtain the expected travel cost required for at least one candidate travel route, which will be used for subsequent operations such as selecting the target travel route.

[0109] S204: Based on at least one expected travel cost obtained, select a target travel path that meets the preset travel cost conditions from at least one candidate travel path.

[0110] Specifically, when performing step S204, after obtaining the expected travel costs required for each of the at least one candidate travel path, the route selection system can obtain the travel cost ranking order corresponding to each of the at least one candidate travel path based on the at least one expected travel cost, and then select the target travel path that meets the preset travel cost conditions from the at least one candidate travel path based on the obtained at least one travel cost ranking order.

[0111] For example, see Figure 7The diagram illustrates a specific application scenario for filtering target driving routes according to an embodiment of this application. After obtaining the expected driving costs (in yuan) of at least one candidate driving route (e.g., Cda.Dr.Route1, Cda.Dr.Route2, Cda.Dr.Route3, and Cda.Dr.Route4), the route selection system can obtain the corresponding costs of at least one candidate driving route based on at least one expected driving cost (i.e., 524, 657, 589, and 624). The order of travel costs is 4, 1, 3, 2. Then, based on at least one obtained order of travel costs (i.e., 4, 1, 3, 2), the target travel path that meets the preset travel cost condition Dri.Cost.Condition is selected from at least one candidate travel path (i.e., Cda.Dr.Route1, Cda.Dr.Route2, Cda.Dr.Route3 and Cda.Dr.Route4), that is, the candidate travel path Cda.Dr.Route1 with the lowest travel cost order is selected as the target travel path.

[0112] Based on the above methods and steps, please refer to Figure 8 The diagram illustrates a specific application scenario of a target fusion method provided in this application. The path selection system acquires the vehicle's current location information Cur.Loc.Infor and the target location information Tar.Loc.Infor of the driving destination. Then, based on the current location information Cur.Loc.Infor and the target location information Tar.Loc.Infor, at least one candidate driving path (Cda.Dr.Route1, Cda.Dr.Route2, Cda.Dr.Route3, and Cda.Dr.Route4) is generated. Further, based on the path feature sets of each of the at least one candidate driving path (in order: Path.Fe...),... Each of the following methods (Path.Feature.Set1, Path.Feature.Set2, Path.Feature.Set3, and Path.Feature.Set4) determines the expected travel cost required for the vehicle on at least one candidate travel path (in order: Exp.Dri.Cost1, Exp.Dri.Cost2, Exp.Dri.Cost3, and Exp.Dri.Cost4); finally, based on the obtained at least one expected travel cost, the target travel path that meets the preset travel cost condition Dri.Cost.Condition is selected from the at least one candidate travel path as Cda.Dr.Route2.

[0113] Optionally, based on the above cost calculation scheme, the route selection system uses the predictive energy management controller to estimate the cost of all candidate driving routes, and finally selects the route with the lowest cost as the cost-optimal route, i.e. the target driving route; and returns the number of the target driving route and the total cost to the navigation APP, which displays it on the vehicle's infotainment screen.

[0114] In summary, the driving route selection method provided in this application embodiment obtains the vehicle's current location information and the target location information of the driving destination; then, based on the current location information and the target location information, at least one candidate driving route is generated; further, based on the path feature sets of each of the at least one candidate driving route, the expected driving cost required by the vehicle on each of the at least one candidate driving route is determined; finally, based on the obtained at least one expected driving cost, a target driving route that meets the preset driving cost conditions is selected from the at least one candidate driving route.

[0115] This approach determines the expected travel cost of a vehicle on each of the at least one candidate travel path based on the path feature set of each candidate travel path. Then, based on the obtained expected travel cost, a target travel path that meets the preset travel cost conditions is selected from the at least one candidate travel path. This avoids the technical drawback of existing technologies that cannot accurately obtain the path travel cost of each candidate vehicle travel path. Therefore, it is used to accurately select the target travel path with the lowest path travel cost.

[0116] Furthermore, based on the same technical concept, this application embodiment also provides a driving route selection device, which is used to implement the driving route selection method flow described above in this application embodiment. See also... Figure 9 As shown, the route selection device includes: a response module 901, a generation module 902, a determination module 903, and a filtering module 904, wherein:

[0117] The response module 901 is used to obtain the vehicle's current location information and the target location information of the driving destination;

[0118] The generation module 902 is used to generate at least one candidate driving path based on the current location information and the target location information;

[0119] The determination module 903 is used to determine the expected driving cost required by the vehicle on each of the at least one candidate driving path based on the path feature set of each of the at least one candidate driving path.

[0120] The filtering module 904 is used to filter out target driving paths that meet preset driving cost conditions from at least one candidate driving path based on at least one obtained expected driving cost.

[0121] In one possible embodiment, during step three, the determining module 903 is specifically used for:

[0122] For at least one candidate driving route, perform the following operations respectively:

[0123] From the path feature set of a candidate driving path, obtain a subset of the slope information of the candidate driving path;

[0124] Based on the slope of each road included in the obtained slope information subset and the slope interval to which each road belongs, a candidate driving route is divided to obtain at least one candidate driving segment.

[0125] Determine the required sub-energy consumption for each of at least one candidate travel segment, and based on the obtained at least one sub-energy consumption, determine the energy consumption cost of a candidate travel route;

[0126] Based on energy consumption costs and road travel costs corresponding to a candidate travel path, the expected travel cost required for a vehicle on a candidate travel path is determined.

[0127] In one possible embodiment, the slope information subset includes the slope information collected by each road information sampling point on a candidate driving path;

[0128] After obtaining a subset of slope information for a candidate driving path, the determining module 903 is further configured to:

[0129] For each slope setting, perform the following operations:

[0130] Obtain the original road segment containing slope information;

[0131] If there are depressions in the original road segment that meet the preset depression conditions, the original road segment is smoothed to obtain the corresponding target road segment.

[0132] In one possible embodiment, when dividing a candidate driving path based on the road slopes contained in the obtained slope information subset and the slope intervals to which each belongs, and obtaining at least one candidate driving segment, the determining module 903 is specifically used for:

[0133] For each subset of slope information, for every at least two adjacent pairs of slope information, perform the following operations:

[0134] If at least two adjacent slope information entries contain the same road slope and belong to the same slope range, then the target road segments corresponding to the at least two slope information entries will be merged to obtain the corresponding candidate driving road segments.

[0135] In one possible embodiment, after merging the target road segments contained in at least two slope information sources to obtain corresponding candidate driving road segments, the determining module 903 is further configured to:

[0136] Based on the road slopes of at least two target road segments, the road slope of the candidate driving road segment is obtained, and based on the road lengths of at least two target road segments, the road length of the candidate driving road segment is obtained.

[0137] In one possible embodiment, when determining the sub-energy consumption required for each of the at least one candidate travel segment, the determining module 903 is specifically used for:

[0138] For at least one candidate travel segment, perform the following operations respectively:

[0139] Based on the path feature set of a candidate driving route, the driving influencing factors of a candidate driving segment are obtained; where the driving influencing factors represent: the environmental information of a candidate driving segment;

[0140] Based on driving-related factors, the vehicle speed and length of a candidate driving segment are set for each candidate driving segment to determine the sub-energy consumption required by the vehicle in that segment.

[0141] In one possible embodiment, during step four, the filtering module 904 is specifically used for:

[0142] Based on at least one expected driving cost, obtain the driving cost ranking order corresponding to at least one candidate driving path;

[0143] Based on at least one obtained ranking of driving costs, a target driving path that meets the preset driving cost conditions is selected from at least one candidate driving path.

[0144] Based on the same technical concept, embodiments of this application also provide an electronic device that can implement the driving route selection method flow provided in the above embodiments of this application. In one embodiment, the electronic device can be a server, a terminal device, or other electronic devices. Figure 10 As shown, the electronic device may include:

[0145] At least one processor 1001 and a memory 1002 connected to at least one processor 1001. In this embodiment, the specific connection medium between the processor 1001 and the memory 1002 is not limited. Figure 10 The example shown is the connection between processor 1001 and memory 1002 via bus 1000. Bus 1000 is... Figure 10 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. The Bus 1000 can be divided into address bus, data bus, control bus, etc., for ease of representation. Figure 10 The term 1001 is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, the processor 1001 can also be called a controller; there are no restrictions on the name.

[0146] In this embodiment, memory 1002 stores instructions executable by at least one processor 1001. By executing the instructions stored in memory 1002, at least one processor 1001 can execute a driving path selection method described above. Processor 1001 can implement... Figure 9 The functions of each module in the device shown.

[0147] The processor 1001 is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory 1002 and calling data stored in memory 1002, the processor can perform various functions and process data, thereby monitoring the device as a whole.

[0148] In one possible design, processor 1001 may include one or more processing units. Processor 1001 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into processor 1001. In some embodiments, processor 1001 and memory 1002 may be implemented on the same chip; in some embodiments, they may also be implemented on separate chips.

[0149] The processor 1001 can be a general-purpose processor, such as a CPU, digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of a driving path selection method disclosed in the embodiments of this application can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0150] Memory 1002, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory 1002 may include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic memory, magnetic disk, optical disk, etc. Memory 1002 can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. In the embodiments of this application, memory 1002 can also be a circuit or any other device capable of implementing storage functions for storing program instructions and / or data.

[0151] By designing and programming the processor 1001, the code corresponding to the driving path selection method described in the foregoing embodiments can be embedded into the chip, thereby enabling the chip to execute the code during operation. Figure 2 The illustrated embodiment presents the steps of a driving route selection method. How to design and program the processor 1001 is a technique well-known to those skilled in the art and will not be described further here.

[0152] Based on the same inventive concept, embodiments of this application also provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform a driving path selection method described above.

[0153] In some possible implementations, various aspects of the driving route selection method provided by this application can also be implemented in the form of a program product, which includes program code that, when the program product is run on a device, causes the control device to perform the steps in a driving route selection method according to various exemplary embodiments of this application described above.

[0154] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0155] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0156] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0157] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for selecting a driving route, characterized in that, include: Step 1: Obtain the vehicle's current location information and the target location information of the driving destination; Step 2: Based on the current location information and the target location information, generate at least one candidate driving path; Step 3: For each of the at least one candidate driving path, perform the following operations: From the path feature set of a candidate driving route, obtain a subset of the slope information of the candidate driving route; based on the slope of each road included in the obtained slope information subset and the slope interval to which each belongs, divide the candidate driving route to obtain at least one candidate driving segment. The sub-energy consumption required for each of the at least one candidate driving segment is determined, and the total energy consumption of the candidate driving path is determined based on the obtained at least one sub-energy consumption. Based on the information of energy replenishment points, the total energy consumption is allocated to fuel replenishment and charging / swapping according to a preset ratio to determine the energy consumption cost; based on the energy consumption cost and the road driving cost corresponding to the candidate driving path, the expected driving cost required by the vehicle on the candidate driving path is determined. Step 4: Based on the obtained at least one expected driving cost, select a target driving path that meets the preset driving cost conditions from the at least one candidate driving path.

2. The method as described in claim 1, characterized in that, The slope information subset includes the slope information collected by each road information sampling point on the candidate driving path; After obtaining the subset of slope information for the candidate driving path, the method further includes: For each slope setting, perform the following operations: Obtain the original road segment containing slope information; If there is a sunken road in the original road segment that meets the preset depression conditions, then the original road segment is smoothed to obtain the corresponding target road segment.

3. The method as described in claim 1, characterized in that, The candidate driving path is divided based on the slope of each road included in the obtained slope information subset and the slope interval to which each road belongs, to obtain at least one candidate driving segment, including: For each set of slope information contained in the slope information subset, for every at least two adjacent pairs of slope information, the following operations are performed: If at least two adjacent slope information entries contain the same road slope and belong to the same slope range, then the target road segments corresponding to the at least two slope information entries are merged to obtain the corresponding candidate driving road segments.

4. The method as described in claim 3, characterized in that, After merging the target road segments contained in each of the at least two slope information sources to obtain the corresponding candidate driving road segments, the method further includes: Based on the road slopes of at least two target road segments, the road slope of the candidate driving segment is obtained, and based on the road lengths of the at least two target road segments, the road length of the candidate driving segment is obtained.

5. The method according to any one of claims 1-4, characterized in that, Determining the required sub-energy consumption for each of the at least one candidate driving segment includes: For each of the at least one candidate travel segment, perform the following operations: Based on the path feature set of the candidate driving route, driving influencing factors of a candidate driving segment are obtained; wherein, the driving influencing factors represent: the environmental information of the candidate driving segment; Based on the driving influencing factors, the vehicle speed set for a candidate driving segment and the segment length of the candidate driving segment are used to determine the sub-energy consumption required by the vehicle in the candidate driving segment.

6. The method as described in claim 1, characterized in that, Step four includes: Based on the at least one expected driving cost, obtain the driving cost ranking order corresponding to each of the at least one candidate driving paths; Based on the obtained at least one driving cost ranking order, a target driving path that meets the preset driving cost conditions is selected from the at least one candidate driving path.

7. A driving route selection device, characterized in that, include: The response module is used to obtain the vehicle's current location information and the target location information of the driving destination; The generation module is used to generate at least one candidate driving path based on the current location information and the target location information; The determination module is configured to perform the following operations for each of the at least one candidate driving path: From the path feature set of a candidate driving route, obtain a subset of the slope information of the candidate driving route; based on the slope of each road included in the obtained slope information subset and the slope interval to which each belongs, divide the candidate driving route to obtain at least one candidate driving segment. The sub-energy consumption required for each of the at least one candidate driving segment is determined, and the total energy consumption of the candidate driving path is determined based on the obtained at least one sub-energy consumption. Based on the information of energy replenishment points, the total energy consumption is allocated to fuel replenishment and charging / swapping according to a preset ratio to determine the energy consumption cost; based on the energy consumption cost and the road driving cost corresponding to the candidate driving path, the expected driving cost required by the vehicle on the candidate driving path is determined. The filtering module is used to filter out target driving paths that meet preset driving cost conditions from the at least one candidate driving path based on at least one obtained expected driving cost.

8. The apparatus as claimed in claim 7, characterized in that, The slope information subset includes the slope information collected by each road information sampling point on the candidate driving path; After obtaining the subset of slope information for the candidate driving path, the determining module is further configured to: For each slope setting, perform the following operations: Obtain the original road segment containing slope information; If there is a sunken road in the original road segment that meets the preset depression conditions, then the original road segment is smoothed to obtain the corresponding target road segment.

9. The apparatus as claimed in claim 7, characterized in that, When dividing a candidate driving path based on the slope of each road included in the obtained slope information subset and the slope interval to which each road belongs, and obtaining at least one candidate driving segment, the determining module is specifically used for: For each set of slope information contained in the slope information subset, for every at least two adjacent pairs of slope information, the following operations are performed: If at least two adjacent slope information entries contain the same road slope and belong to the same slope range, then the target road segments corresponding to the at least two slope information entries are merged to obtain the corresponding candidate driving road segments.

10. The apparatus as claimed in claim 9, characterized in that, After merging the target road segments contained in each of the at least two slope information sets to obtain the corresponding candidate driving road segments, the determining module is further configured to: Based on the road slopes of at least two target road segments, the road slope of the candidate driving segment is obtained, and based on the road lengths of the at least two target road segments, the road length of the candidate driving segment is obtained.

11. The apparatus according to any one of claims 7-10, characterized in that, When determining the required sub-energy consumption for each of the at least one candidate travel segment, the determining module is specifically used for: For each of the at least one candidate travel segment, perform the following operations: Based on the path feature set of the candidate driving route, driving influencing factors of a candidate driving segment are obtained; wherein, the driving influencing factors represent: the environmental information of the candidate driving segment; Based on the driving influencing factors, the vehicle speed set for a candidate driving segment and the segment length of the candidate driving segment are used to determine the sub-energy consumption required by the vehicle in the candidate driving segment.

12. The apparatus as claimed in claim 7, characterized in that, When determining the expected travel cost required by the vehicle for each of the at least one candidate travel path based on the path feature sets of each of the at least one candidate travel path, the filtering module is specifically used for: Based on the at least one expected driving cost, obtain the driving cost ranking order corresponding to each of the at least one candidate driving paths; Based on the obtained at least one driving cost ranking order, a target driving path that meets the preset driving cost conditions is selected from the at least one candidate driving path.

13. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-6.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-6.

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